Detail of Publication

Text Language	English
Authors	Yuki Daiku, Olivier Augereau, Motoi Iwata, Koichi Kise
Title	Comic story analysis based on genre classification
Book_Title	Proceedings of 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)
Vol.	03
Pages	pp.60-65
Number of Pages	6 pages
Publisher	IEE
Reviewed or not	Reviewed
Month & Year	November 2017
Abstract	Comic readers are attracted to not only pictures of unique characters or beautiful landscape but also deliberated story. Understanding comic story is helpful for a comic retrieval, which allows readers to obtain comics suited to readers臓�� interest, or creative activities, which demand to generate interesting idea of comic narratives. Therefore, as the method of understanding comic story, we propose a converting method from a real comic story into a novel formatted narrative structure, which uses comic genres as the representation of contents of story. In a converting method, each page in a comic volume is classified into what genre the page portrays by using convolutional neural network. Generally, in machine learning, labeling ground truth on a large number of training samples is necessary, which spends much costs of time and money. In this paper, we propose a learning way, which supports to label ground truth on such many samples. The experimental results show the effectiveness of our proposed converting method.

• Entry for BibTeX

  @InProceedings{Daiku2017,
    author =	{Yuki Daiku and Olivier Augereau and Motoi Iwata and Koichi Kise},
    title =	{Comic story analysis based on genre classification},
    book_title =	{Proceedings of 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)},
    year =	2017,
    month =	nov,
    volume =	{03},
    pages =	{60--65},
    numpages =	{6},
    publisher =	{IEE}
  }
  
  
  

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